Madness 2024

Code

ratings <- read_sheet("1UYGzX2raigszjKffPPKf3oZZjVkU61c27Ppatkdm0so", sheet = "2024 schedule") %>%
  mutate(Date = ymd(Date)) %>% filter(Date <= ymd("2024.3.18")) %>% 
  select(album, Charlie:Tyler)

uu <- Cs(Charlie, Josh, Justin, Tyler)

totals <- ratings %>% select(album, all_of(uu) ) %>% 
  pivot_longer(., -1) %>% group_by(name) %>% 
  summarise(Total = sum(value)) %>% arrange(desc(Total))

ratings %<>% 
  mutate(across(all_of(uu), ~10*(.x-min(.x))/(max(.x)-min(.x)), .names =  "{.col}_st")) %>% 
  # mutate(across(all_of(uu), ~./sum(.x), .names =  "{.col}_st")) %>% 
  # mutate(across(contains("_st"), ~.x*max(totals$Total))) %>% 
  rowwise() %>% 
  mutate(total = sum(across(all_of(uu))),
         adj_total = sum(across(ends_with("_st")))) %>% ungroup() %>% 
  arrange(desc(total)) %>% mutate(raw_rank = 1:n()) %>% 
  arrange(desc(adj_total)) %>% mutate(adj_rank = 1:n()) %>% 
  rowwise() %>% 
  mutate(min_rating = min(across(ends_with("_st"))), t2 = total -min_rating) %>% ungroup() %>% 
  arrange(desc(t2)) %>% mutate(adj_rank_2 = 1:n()) %>% 
  arrange(desc(Charlie)) %>% mutate(charlie_rank = min_rank(desc(Charlie))) %>% 
  arrange(desc(Josh)) %>% mutate(josh_rank = min_rank(desc(Josh))) %>% 
  arrange(desc(Justin)) %>% mutate(justin_rank = min_rank(desc(Justin))) %>%
  arrange(desc(Tyler)) %>% mutate(tyler_rank = min_rank(desc(Tyler))) %>% 
  mutate(across(ends_with("_rank"), ~ifelse(.x <=20, "Top 20ish", "Nope"), .names = "{.col}_class")) %>% rowwise() %>% 
  mutate(agg_advance = sum(across(all_of(c("tyler_rank_class", "josh_rank_class", "justin_rank_class", "charlie_rank_class")), ~sum(.x == "Top 20ish")))) %>% ungroup()

# (value - 1.05) / (9.25 - 1.05)

mad_sheet <- read_sheet("1UYGzX2raigszjKffPPKf3oZZjVkU61c27Ppatkdm0so", sheet = "2024 albums") %>%
  mutate(artist = trimws(artist)) %>% 
  mutate(a1 = tolower(artist), a1 = stri_trans_general(a1, 'latin-ascii')) %>% 
  mutate(a2 = tolower(album), a2 = stri_trans_general(a2, 'latin-ascii')) %>% select(-n, -last_played)

mad_sheet %<>% mutate(popularity = log(monthly*1000, 10)) %>% mutate(listen_date = ymd(listen_date), 
                                                                     all_music = as.numeric(all_music))

mad_sheet %<>% left_join(., 
                       ratings,
                       by = "album")

Code

scrobbles <- lastfmR::get_scrobbles(user = "jjdeclercq")
mad_tal <- scrobbles %>% mutate(date = ymd(as.Date(date))) %>% filter(date > ymd("2024.1.1")) %>% 
  group_by(artist, album) %>%summarise(n = n(), last_played = max(date))%>%
  separate(., album, into = c("album", "extra2"), sep = " \\[", fill = "right") %>% 
  separate(., album, into = c("album", "extra"), sep = " \\(", fill = "right") %>% 
  group_by(artist, album) %>% summarise(n = sum(n), last_played = max(last_played))%>% 
  left_join(., fix_artists,  by = c("artist")) %>% mutate(artist = ifelse(!is.na(fix.a), fix.a, artist)) %>% 
    mutate(a1 = tolower(artist), a1 = stri_trans_general(a1, 'latin-ascii')) %>% 
    left_join(., fix_albums,  by = c("a1", "album")) %>% mutate(album = ifelse(!is.na(fix), fix, album)) %>% 
  mutate(a2 = tolower(album), a2 = stri_trans_general(a2, 'latin-ascii')) %>% 
  arrange(a1, a2, desc(n)) %>% 
  group_by(a1, a2) %>% summarise(n = sum(n), album = first(album), artist = first(artist), last_played = max(last_played))


mad_add <- left_join(mad_sheet , mad_tal %>% select(-artist, -album), by = c("a1", "a2")) %>% select(-a1, -a2) %>% 
  mutate(n = replace_na(n, 0)) %>% mutate(listens = round(n/tracks,1))


write_sheet(mad_add, "1UYGzX2raigszjKffPPKf3oZZjVkU61c27Ppatkdm0so", sheet = "2024 albums")

Code

mad_songs <- read.csv("madness_2024.csv") %>% namify() %>% 
  rename(album = album_name, artist = artist_name_s_, date = release_date) %>%
  separate(., album, into = c("album", "extra2"), sep = " \\[", fill = "right") %>% 
  separate(., album, into = c("album", "extra"), sep = " \\(", fill = "right")%>% 
  # left_join(., fix_artists,  by = c("artist")) %>% mutate(artist = ifelse(!is.na(fix.a), fix.a, artist)) %>% 
    mutate(a1 = tolower(artist), a1 = stri_trans_general(a1, 'latin-ascii')) %>% 
    # left_join(., fix_albums,  by = c("a1", "album")) %>% mutate(album = ifelse(!is.na(fix), fix, album)) %>% 
  mutate(a2 = tolower(album), a2 = stri_trans_general(a2, 'latin-ascii'),y2 = str_sub(date, 1,4))%>% 
  mutate(a1 = gsub(",.*","",a1))


mad_sheet %<>% left_join(., mad_songs %>% group_by(a1) %>% summarise(liked_songs = n())) %>% 
  mutate(liked_songs = replace_na(liked_songs, 0))

### Add in album art
fa <- data.frame(aa = list.files("png"))

fax <-fuzzyjoin::stringdist_join(mad_sheet %>% select(a1, a2) %>% mutate(aa = paste(a1, a2, sep = "-")), 
                                 fa,
                by='aa', #match based on team
                mode='left', #use left join
                method = "jw", #use jw distance metric
                max_dist=99,
                distance_col='dist')%>% 
  group_by(a2) %>% filter(dist == min(dist)) %>% ungroup() %>% 
  select(a2, art = aa.y)

mad_sheet %<>% left_join(., fax, by = "a2")

1 Albums

Code

cat(paste0("![](png/",mad_sheet$art,"){width=100px}\n"))

Figure 1: Prince

Stats by person

Code

mad_sheet %>% 
  select(raw_rank_class, order, popularity, tracks, length, who, year, g2, liked_songs, all_music, rym) %>% 
  jgt(., by = "who", overall = TRUE, add.p = TRUE, force.continuous = TRUE)

Characteristic	N	Chuck, N = 161	Josh, N = 161	Justin, N = 161	Tyler, N = 161	p-value2	Overall, N = 641
raw_rank_class	64					0.155
Nope		9 (56.3%)	9 (56.3%)	14 (87.5%)	12 (75.0%)		44 (68.8%)
Top 20ish		7 (43.8%)	7 (43.8%)	2 (12.5%)	4 (25.0%)		20 (31.3%)
order	64					0.885
Mean (SD)		34.7 (15.7)	30.1 (20.0)	31.3 (20.6)	33.9 (19.3)		32.5 (18.6)
Median (IQR)		38.0 (22.8 - 45.3)	23.5 (15.3 - 48.5)	31.5 (14.0 - 43.8)	32.0 (20.3 - 50.3)		32.5 (16.8 - 48.3)
Range		3.0 - 56.0	1.0 - 61.0	2.0 - 64.0	5.0 - 62.0		1.0 - 64.0
popularity	64					0.209
Mean (SD)		6.3 (0.7)	6.3 (0.8)	5.9 (0.9)	6.7 (1.0)		6.3 (0.9)
Median (IQR)		6.3 (6.0 - 6.4)	6.4 (5.6 - 6.8)	6.2 (5.5 - 6.4)	6.8 (6.0 - 7.4)		6.3 (5.8 - 7.0)
Range		4.4 - 8.0	4.9 - 8.0	4.4 - 7.7	4.9 - 8.0		4.4 - 8.0
tracks	64					0.673
Mean (SD)		13.7 (3.1)	12.6 (3.4)	13.1 (2.7)	13.9 (3.7)		13.3 (3.2)
Median (IQR)		13.5 (12.0 - 14.3)	12.0 (11.0 - 14.3)	12.5 (11.0 - 16.0)	14.5 (11.0 - 16.0)		13.0 (11.0 - 15.3)
Range		9.0 - 22.0	7.0 - 21.0	9.0 - 18.0	8.0 - 22.0		7.0 - 22.0
length	64					0.823
Mean (SD)		47.8 (13.1)	43.9 (8.8)	47.6 (13.2)	44.9 (12.4)		46.0 (11.9)
Median (IQR)		47.0 (38.8 - 53.8)	41.0 (37.0 - 52.3)	46.0 (35.8 - 52.5)	43.5 (39.5 - 52.8)		44.0 (37.8 - 53.0)
Range		23.0 - 73.0	34.0 - 61.0	33.0 - 78.0	25.0 - 75.0		23.0 - 78.0
year	64					0.030
Mean (SD)		1995.8 (16.8)	1996.0 (20.0)	2012.6 (8.6)	2003.2 (17.7)		2001.9 (17.4)
Median (IQR)		1997.5 (1981.5 - 2000.3)	1993.5 (1978.5 - 2019.3)	2014.5 (2006.0 - 2019.8)	2011.0 (1982.8 - 2017.0)		2004.5 (1987.5 - 2017.5)
Range		1970.0 - 2024.0	1970.0 - 2024.0	1999.0 - 2023.0	1969.0 - 2023.0		1969.0 - 2024.0
g2	64					0.211
alternative, indie & pop		2 (12.5%)	3 (18.8%)	3 (18.8%)	2 (12.5%)		10 (15.6%)
country, folk, americana & blues		2 (12.5%)	4 (25.0%)	2 (12.5%)	4 (25.0%)		12 (18.8%)
funk, soul and R&B		2 (12.5%)	3 (18.8%)	0 (0%)	3 (18.8%)		8 (12.5%)
punk, rock & metal		8 (50.0%)	5 (31.3%)	3 (18.8%)	3 (18.8%)		19 (29.7%)
rap & hip hop		2 (12.5%)	1 (6.25%)	8 (50.0%)	4 (25.0%)		15 (23.4%)
liked_songs	64					0.466
Mean (SD)		0.9 (1.4)	1.5 (1.4)	0.8 (0.9)	1.1 (1.3)		1.1 (1.3)
Median (IQR)		0.0 (0.0 - 2.0)	1.0 (0.0 - 2.3)	1.0 (0.0 - 1.0)	0.5 (0.0 - 2.0)		1.0 (0.0 - 2.0)
Range		0.0 - 4.0	0.0 - 4.0	0.0 - 3.0	0.0 - 4.0		0.0 - 4.0
all_music	56					0.031
Mean (SD)		4.1 (0.8)	4.4 (0.6)	4.0 (0.8)	3.6 (0.7)		4.0 (0.8)
Median (IQR)		4.3 (3.5 - 4.5)	4.5 (4.0 - 5.0)	4.0 (3.5 - 4.5)	3.5 (3.0 - 4.3)		4.0 (3.5 - 4.5)
Range		2.5 - 5.0	3.0 - 5.0	2.5 - 5.0	3.0 - 5.0		2.5 - 5.0
Missing		0	1	5	2		8
rym	64					0.006
Mean (SD)		3.4 (0.8)	3.7 (0.4)	3.5 (0.5)	3.2 (0.5)		3.4 (0.6)
Median (IQR)		3.6 (3.5 - 3.7)	3.8 (3.6 - 3.9)	3.5 (3.4 - 3.8)	3.3 (2.7 - 3.6)		3.6 (3.4 - 3.8)
Range		0.9 - 4.1	2.7 - 4.2	2.2 - 4.4	2.4 - 3.8		0.9 - 4.4
1 n (%)
2 Pearson’s Chi-squared test; Kruskal-Wallis rank sum test; Fisher’s exact test

Stats by genre

Code

mad_sheet %>% 
  select(raw_rank_class,order, popularity, tracks, length, who, year, g2, liked_songs, all_music, rym) %>% 
  jgt(., by = "g2", overall = TRUE, add.p = TRUE)

Characteristic	N	alternative, indie & pop, N = 101	country, folk, americana & blues, N = 121	funk, soul and R&B, N = 81	punk, rock & metal, N = 191	rap & hip hop, N = 151	p-value2	Overall, N = 641
raw_rank_class	64						0.132
Nope		9 (90.0%)	9 (75.0%)	5 (62.5%)	9 (47.4%)	12 (80.0%)		44 (68.8%)
Top 20ish		1 (10.0%)	3 (25.0%)	3 (37.5%)	10 (52.6%)	3 (20.0%)		20 (31.3%)
order	64						0.501
Mean (SD)		27.7 (20.4)	27.3 (16.3)	33.1 (18.4)	38.2 (19.1)	32.3 (18.9)		32.5 (18.6)
Median (IQR)		20.0 (14.3 - 40.8)	26.5 (12.8 - 42.8)	39.0 (22.8 - 45.5)	41.0 (21.5 - 55.0)	30.0 (23.0 - 43.0)		32.5 (16.8 - 48.3)
Range		2.0 - 60.0	4.0 - 51.0	5.0 - 53.0	3.0 - 64.0	1.0 - 63.0		1.0 - 64.0
popularity	64						0.004
Mean (SD)		7.1 (0.8)	6.1 (0.8)	6.6 (0.5)	5.8 (0.8)	6.4 (0.9)		6.3 (0.9)
Median (IQR)		7.0 (6.3 - 8.0)	6.1 (5.6 - 6.6)	6.6 (6.1 - 7.1)	6.0 (5.3 - 6.3)	6.4 (5.7 - 7.3)		6.3 (5.8 - 7.0)
Range		6.2 - 8.0	4.9 - 7.5	6.0 - 7.1	4.4 - 7.0	4.8 - 7.8		4.4 - 8.0
tracks	64						0.007
Mean (SD)		12.6 (2.9)	12.0 (1.3)	11.0 (3.0)	13.8 (3.6)	15.5 (2.9)		13.3 (3.2)
Median (IQR)		12.0 (11.0 - 14.5)	12.0 (11.0 - 13.0)	11.0 (8.8 - 12.8)	14.0 (11.0 - 15.5)	16.0 (13.5 - 16.5)		13.0 (11.0 - 15.3)
Range		9.0 - 17.0	10.0 - 14.0	7.0 - 15.0	8.0 - 22.0	11.0 - 22.0		7.0 - 22.0
length	64						0.009
Mean (SD)		48.7 (11.6)	43.3 (6.6)	38.5 (4.5)	42.6 (12.6)	54.8 (12.5)		46.0 (11.9)
Median (IQR)		51.0 (37.3 - 56.0)	43.0 (39.5 - 44.0)	38.5 (37.0 - 40.3)	40.0 (34.5 - 52.5)	51.0 (47.0 - 61.5)		44.0 (37.8 - 53.0)
Range		33.0 - 67.0	36.0 - 61.0	30.0 - 46.0	23.0 - 73.0	34.0 - 78.0		23.0 - 78.0
who	64						0.211
Chuck		2 (20.0%)	2 (16.7%)	2 (25.0%)	8 (42.1%)	2 (13.3%)		16 (25.0%)
Josh		3 (30.0%)	4 (33.3%)	3 (37.5%)	5 (26.3%)	1 (6.67%)		16 (25.0%)
Justin		3 (30.0%)	2 (16.7%)	0 (0%)	3 (15.8%)	8 (53.3%)		16 (25.0%)
Tyler		2 (20.0%)	4 (33.3%)	3 (37.5%)	3 (15.8%)	4 (26.7%)		16 (25.0%)
year	64						0.002
Mean (SD)		2012.6 (10.1)	2000.7 (18.0)	1986.3 (19.8)	1994.9 (16.1)	2013.0 (9.4)		2001.9 (17.4)
Median (IQR)		2015.5 (2008.0 - 2019.3)	2001.5 (1994.8 - 2013.3)	1979.5 (1972.5 - 1990.3)	1994.0 (1982.0 - 2002.5)	2015.0 (2006.0 - 2020.0)		2004.5 (1987.5 - 2017.5)
Range		1989.0 - 2023.0	1969.0 - 2023.0	1970.0 - 2020.0	1973.0 - 2024.0	1998.0 - 2024.0		1969.0 - 2024.0
liked_songs	64
0		4 (40.0%)	4 (33.3%)	4 (50.0%)	11 (57.9%)	7 (46.7%)		30 (46.9%)
1		5 (50.0%)	3 (25.0%)	2 (25.0%)	0 (0%)	3 (20.0%)		13 (20.3%)
2		1 (10.0%)	3 (25.0%)	2 (25.0%)	4 (21.1%)	1 (6.67%)		11 (17.2%)
3		0 (0%)	0 (0%)	0 (0%)	3 (15.8%)	3 (20.0%)		6 (9.38%)
4		0 (0%)	2 (16.7%)	0 (0%)	1 (5.26%)	1 (6.67%)		4 (6.25%)
all_music	56
2.5		0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (16.7%)		2 (3.57%)
3		1 (10.0%)	1 (11.1%)	2 (25.0%)	4 (23.5%)	1 (8.33%)		9 (16.1%)
3.5		0 (0%)	3 (33.3%)	0 (0%)	4 (23.5%)	3 (25.0%)		10 (17.9%)
4		4 (40.0%)	2 (22.2%)	0 (0%)	2 (11.8%)	3 (25.0%)		11 (19.6%)
4.5		1 (10.0%)	2 (22.2%)	4 (50.0%)	4 (23.5%)	0 (0%)		11 (19.6%)
5		4 (40.0%)	1 (11.1%)	2 (25.0%)	3 (17.6%)	3 (25.0%)		13 (23.2%)
Missing		0	3	0	2	3		8
rym	64						0.963
Mean (SD)		3.4 (0.6)	3.5 (0.4)	3.6 (0.2)	3.6 (0.3)	3.2 (0.9)		3.4 (0.6)
Median (IQR)		3.6 (2.9 - 3.9)	3.6 (3.4 - 3.7)	3.5 (3.4 - 3.8)	3.6 (3.4 - 3.7)	3.5 (2.6 - 3.8)		3.6 (3.4 - 3.8)
Range		2.4 - 4.0	2.4 - 4.2	3.4 - 4.0	3.0 - 4.1	0.9 - 4.4		0.9 - 4.4
1 n (%)
2 Fisher’s exact test; Kruskal-Wallis rank sum test

1.1 Popularity by year and genre

Code

mad_sheet %>% 
  ggplot(., aes(x= year, y = popularity, colour = g2)) +geom_point() +
  facet_wrap(~who) +theme_bw()

1.2 Albums details

Code

mad_sheet  %>% 
  select(art,raw_rank, adj_rank, adj_rank_2, artist, album, order, who, year:liked_songs) %>% 
  j.reactable(
  .,
  columns = list(
    art = colDef(cell = function(value) {
      image <- img(src = sprintf("png/%s", value), style = "height: 100px;", alt = "")
      tagList(
        div(style = "display: inline-block; width: 45px;", image),
        # value
      )})
  )
)

2 Ratings

2.1 Overview

Table

Code

# ColorBrewer-inspired 3-color scale
BuYlRd <- function(x) rgb(colorRamp(c("#7fb7d7", "#ffffbf", "salmon"))(x), maxColorValue = 255)

mad_sheet %>% select(adj_rank, raw_rank,who,g2, album, Charlie:Tyler) %>% 
  mutate(Avg = (Charlie +Josh + Justin +Tyler)/4) %>% 
  na.omit() %>% 
  mutate(raw_rank = ifelse(raw_rank <10, paste0("0",raw_rank), as.character(raw_rank))) %>% 
  mutate(adj_rank = ifelse(adj_rank <10, paste0("0",adj_rank), as.character(adj_rank))) %>% 
  j.reactable(., 
              defaultColDef = colDef(
    style = function(value) {
      if (!is.numeric(value)) return()
      normalized <- (value - 1.05) / (9.25 - 1.05)
      color <- BuYlRd(normalized)
      list(background = color)
    },
    format = colFormat(digits = 1),
    minWidth = 50
  ), defaultPageSize = 64
  )

Boxplot

Code

mad_sheet %>% select(album, all_of(uu) ) %>% 
  pivot_longer(., -1) %>% 
  ggplot(., aes(x = name, y = value, colour = name)) + geom_boxplot() +geom_beeswarm() +theme_minimal() + guides(colour = "none")

2.2 Advanced to next round

By overall advancement

Code

mad_sheet %>% select(ends_with("rank_class"), agg_advance, who) %>% 
  jgt(., by = "adj_rank_class", add.p = TRUE, overall = TRUE)

Characteristic	N	Nope, N = 441	Top 20ish, N = 201	p-value2	Overall, N = 641
raw_rank_class	64			<0.001
Nope		44 (100.0%)	0 (0%)		44 (68.8%)
Top 20ish		0 (0%)	20 (100.0%)		20 (31.3%)
charlie_rank_class	64			<0.001
Nope		39 (88.6%)	5 (25.0%)		44 (68.8%)
Top 20ish		5 (11.4%)	15 (75.0%)		20 (31.3%)
josh_rank_class	64			<0.001
Nope		38 (86.4%)	6 (30.0%)		44 (68.8%)
Top 20ish		6 (13.6%)	14 (70.0%)		20 (31.3%)
justin_rank_class	64			<0.001
Nope		37 (84.1%)	5 (25.0%)		42 (65.6%)
Top 20ish		7 (15.9%)	15 (75.0%)		22 (34.4%)
tyler_rank_class	64			<0.001
Nope		37 (84.1%)	7 (35.0%)		44 (68.8%)
Top 20ish		7 (15.9%)	13 (65.0%)		20 (31.3%)
agg_advance	64			<0.001
0		25 (56.8%)	0 (0%)		25 (39.1%)
1		13 (29.5%)	1 (5.00%)		14 (21.9%)
2		6 (13.6%)	7 (35.0%)		13 (20.3%)
3		0 (0%)	6 (30.0%)		6 (9.38%)
4		0 (0%)	6 (30.0%)		6 (9.38%)
who	64			0.155
Chuck		9 (20.5%)	7 (35.0%)		16 (25.0%)
Josh		9 (20.5%)	7 (35.0%)		16 (25.0%)
Justin		14 (31.8%)	2 (10.0%)		16 (25.0%)
Tyler		12 (27.3%)	4 (20.0%)		16 (25.0%)
1 n (%)
2 Pearson’s Chi-squared test; Fisher’s exact test

By person selecting album

Code

mad_sheet %>% select(ends_with("rank_class"), agg_advance, who) %>% 
  jgt(., by = "who", add.p = TRUE, overall = TRUE)

Characteristic	N	Chuck, N = 161	Josh, N = 161	Justin, N = 161	Tyler, N = 161	p-value2	Overall, N = 641
raw_rank_class	64					0.155
Nope		9 (56.3%)	9 (56.3%)	14 (87.5%)	12 (75.0%)		44 (68.8%)
Top 20ish		7 (43.8%)	7 (43.8%)	2 (12.5%)	4 (25.0%)		20 (31.3%)
adj_rank_class	64					0.155
Nope		9 (56.3%)	9 (56.3%)	14 (87.5%)	12 (75.0%)		44 (68.8%)
Top 20ish		7 (43.8%)	7 (43.8%)	2 (12.5%)	4 (25.0%)		20 (31.3%)
charlie_rank_class	64					0.011
Nope		6 (37.5%)	11 (68.8%)	14 (87.5%)	13 (81.3%)		44 (68.8%)
Top 20ish		10 (62.5%)	5 (31.3%)	2 (12.5%)	3 (18.8%)		20 (31.3%)
josh_rank_class	64					0.020
Nope		12 (75.0%)	6 (37.5%)	13 (81.3%)	13 (81.3%)		44 (68.8%)
Top 20ish		4 (25.0%)	10 (62.5%)	3 (18.8%)	3 (18.8%)		20 (31.3%)
justin_rank_class	64					0.842
Nope		10 (62.5%)	10 (62.5%)	10 (62.5%)	12 (75.0%)		42 (65.6%)
Top 20ish		6 (37.5%)	6 (37.5%)	6 (37.5%)	4 (25.0%)		22 (34.4%)
tyler_rank_class	64					0.011
Nope		11 (68.8%)	9 (56.3%)	16 (100.0%)	8 (50.0%)		44 (68.8%)
Top 20ish		5 (31.3%)	7 (43.8%)	0 (0%)	8 (50.0%)		20 (31.3%)
agg_advance	64					0.415
0		4 (25.0%)	5 (31.3%)	10 (62.5%)	6 (37.5%)		25 (39.1%)
1		5 (31.3%)	3 (18.8%)	2 (12.5%)	4 (25.0%)		14 (21.9%)
2		3 (18.8%)	2 (12.5%)	3 (18.8%)	5 (31.3%)		13 (20.3%)
3		2 (12.5%)	3 (18.8%)	1 (6.25%)	0 (0%)		6 (9.38%)
4		2 (12.5%)	3 (18.8%)	0 (0%)	1 (6.25%)		6 (9.38%)
1 n (%)
2 Pearson’s Chi-squared test; Fisher’s exact test

By genre

Code

mad_sheet %>% select(ends_with("rank_class"), agg_advance, g2) %>% 
  jgt(., by = "g2", add.p = TRUE, overall = TRUE)

Characteristic	N	alternative, indie & pop, N = 101	country, folk, americana & blues, N = 121	funk, soul and R&B, N = 81	punk, rock & metal, N = 191	rap & hip hop, N = 151	p-value2	Overall, N = 641
raw_rank_class	64						0.132
Nope		9 (90.0%)	9 (75.0%)	5 (62.5%)	9 (47.4%)	12 (80.0%)		44 (68.8%)
Top 20ish		1 (10.0%)	3 (25.0%)	3 (37.5%)	10 (52.6%)	3 (20.0%)		20 (31.3%)
adj_rank_class	64						0.132
Nope		9 (90.0%)	9 (75.0%)	5 (62.5%)	9 (47.4%)	12 (80.0%)		44 (68.8%)
Top 20ish		1 (10.0%)	3 (25.0%)	3 (37.5%)	10 (52.6%)	3 (20.0%)		20 (31.3%)
charlie_rank_class	64						<0.001
Nope		10 (100.0%)	9 (75.0%)	4 (50.0%)	7 (36.8%)	14 (93.3%)		44 (68.8%)
Top 20ish		0 (0%)	3 (25.0%)	4 (50.0%)	12 (63.2%)	1 (6.67%)		20 (31.3%)
josh_rank_class	64						0.304
Nope		7 (70.0%)	9 (75.0%)	4 (50.0%)	11 (57.9%)	13 (86.7%)		44 (68.8%)
Top 20ish		3 (30.0%)	3 (25.0%)	4 (50.0%)	8 (42.1%)	2 (13.3%)		20 (31.3%)
justin_rank_class	64						0.937
Nope		7 (70.0%)	9 (75.0%)	5 (62.5%)	12 (63.2%)	9 (60.0%)		42 (65.6%)
Top 20ish		3 (30.0%)	3 (25.0%)	3 (37.5%)	7 (36.8%)	6 (40.0%)		22 (34.4%)
tyler_rank_class	64						0.016
Nope		10 (100.0%)	6 (50.0%)	4 (50.0%)	11 (57.9%)	13 (86.7%)		44 (68.8%)
Top 20ish		0 (0%)	6 (50.0%)	4 (50.0%)	8 (42.1%)	2 (13.3%)		20 (31.3%)
agg_advance	64
0		6 (60.0%)	4 (33.3%)	1 (12.5%)	6 (31.6%)	8 (53.3%)		25 (39.1%)
1		2 (20.0%)	5 (41.7%)	2 (25.0%)	2 (10.5%)	3 (20.0%)		14 (21.9%)
2		2 (20.0%)	1 (8.33%)	3 (37.5%)	3 (15.8%)	4 (26.7%)		13 (20.3%)
3		0 (0%)	0 (0%)	1 (12.5%)	5 (26.3%)	0 (0%)		6 (9.38%)
4		0 (0%)	2 (16.7%)	1 (12.5%)	3 (15.8%)	0 (0%)		6 (9.38%)
1 n (%)
2 Fisher’s exact test

Rank data

Code

mad_sheet %>% 
  select(album,agg_advance,raw_rank_class, raw_rank, ends_with("_rank"), agg_advance) %>% 
  j.reactable(., defaultPageSize = 64, filterable = TRUE)

Rank classification data

Code

mad_sheet %>% 
  select(album,agg_advance,raw_rank_class, raw_rank, ends_with("_rank_class"), agg_advance) %>% 
  j.reactable(., defaultPageSize = 64, filterable = TRUE)

2.3 By picker

Raw

Code

mad_sheet %>% select(Charlie:Tyler, who) %>% na.omit() %>% jgt(., by = "who", overall = TRUE, add.p = TRUE, spanner.size = 4, spanner.text = "Album picked by")

Characteristic	N	Album picked by				p-value1	Overall, N = 64
		Chuck, N = 16	Josh, N = 16	Justin, N = 16	Tyler, N = 16
Charlie	64					0.005
Mean (SD)		6.5 (1.4)	5.6 (1.4)	4.6 (1.6)	5.1 (1.5)		5.4 (1.6)
Median (IQR)		6.8 (6.0 - 7.5)	5.7 (4.6 - 6.8)	4.3 (3.8 - 5.2)	5.1 (4.5 - 5.9)		5.6 (4.4 - 6.7)
Range		3.5 - 8.5	2.3 - 7.5	1.2 - 8.1	1.2 - 7.7		1.2 - 8.5
Josh	64					0.010
Mean (SD)		5.7 (2.1)	7.1 (1.1)	5.1 (1.8)	5.2 (1.9)		5.8 (1.9)
Median (IQR)		6.3 (4.8 - 6.7)	7.1 (6.0 - 7.6)	5.0 (3.9 - 6.2)	5.6 (3.8 - 6.5)		6.0 (4.5 - 7.1)
Range		1.1 - 8.4	5.2 - 9.3	1.5 - 8.0	1.5 - 8.0		1.1 - 9.3
Justin	64					0.609
Mean (SD)		5.5 (1.7)	5.9 (1.3)	6.0 (1.5)	5.5 (1.4)		5.7 (1.5)
Median (IQR)		5.3 (4.4 - 7.0)	5.5 (5.0 - 7.1)	5.9 (5.0 - 7.1)	5.8 (4.6 - 6.6)		5.5 (4.8 - 7.0)
Range		2.5 - 8.0	4.0 - 8.0	3.5 - 8.5	3.0 - 7.0		2.5 - 8.5
Tyler	64					0.002
Mean (SD)		5.8 (2.0)	5.8 (1.5)	4.3 (1.3)	6.4 (1.0)		5.6 (1.7)
Median (IQR)		6.0 (4.8 - 7.0)	5.8 (5.0 - 7.0)	4.5 (4.0 - 5.0)	6.5 (5.5 - 7.1)		5.6 (5.0 - 7.0)
Range		1.9 - 9.0	2.5 - 7.6	1.3 - 6.0	5.0 - 8.0		1.3 - 9.0
1 Kruskal-Wallis rank sum test

Standardized

Code

mad_sheet %>% select(ends_with("_st"), who) %>% na.omit() %>% jgt(., by = "who", overall = TRUE, add.p = TRUE, spanner.size = 4, spanner.text = "Album picked by")

Characteristic	N	Album picked by				p-value1	Overall, N = 64
		Chuck, N = 16	Josh, N = 16	Justin, N = 16	Tyler, N = 16
Charlie_st	64					0.005
Mean (SD)		7.2 (1.9)	6.1 (2.0)	4.6 (2.2)	5.3 (2.1)		5.8 (2.2)
Median (IQR)		7.6 (6.6 - 8.6)	6.1 (4.6 - 7.6)	4.2 (3.6 - 5.4)	5.3 (4.5 - 6.4)		6.0 (4.3 - 7.5)
Range		3.2 - 10.0	1.5 - 8.6	0.0 - 9.5	0.0 - 8.9		0.0 - 10.0
Josh_st	64					0.010
Mean (SD)		5.6 (2.5)	7.4 (1.4)	5.0 (2.2)	5.1 (2.4)		5.8 (2.3)
Median (IQR)		6.4 (4.5 - 6.9)	7.4 (6.1 - 8.0)	4.8 (3.5 - 6.3)	5.6 (3.4 - 6.7)		6.1 (4.2 - 7.3)
Range		0.0 - 9.0	5.1 - 10.0	0.5 - 8.5	0.6 - 8.5		0.0 - 10.0
Justin_st	64					0.609
Mean (SD)		5.0 (2.8)	5.6 (2.2)	5.9 (2.5)	4.9 (2.3)		5.4 (2.4)
Median (IQR)		4.6 (3.1 - 7.5)	5.0 (4.2 - 7.6)	5.6 (4.2 - 7.6)	5.4 (3.5 - 6.8)		5.0 (3.8 - 7.5)
Range		0.0 - 9.2	2.5 - 9.2	1.7 - 10.0	0.8 - 7.5		0.0 - 10.0
Tyler_st	64					0.002
Mean (SD)		5.8 (2.6)	5.8 (2.0)	3.9 (1.7)	6.6 (1.3)		5.5 (2.2)
Median (IQR)		6.1 (4.5 - 7.4)	5.8 (4.8 - 7.4)	4.1 (3.5 - 4.8)	6.7 (5.5 - 7.5)		5.6 (4.8 - 7.4)
Range		0.7 - 10.0	1.6 - 8.2	0.0 - 6.1	4.8 - 8.7		0.0 - 10.0
1 Kruskal-Wallis rank sum test

2.4 Rounds of 8

Code

mad_sheet%>% 
  mutate(o2 = cut2(order, cuts = c(9, 17, 25, 33, 41, 49, 57))) %>% 
  select(o2, Charlie:Tyler) %>% 
  pivot_longer(., -1) %>% 
  ggplot(., aes(x = value, colour = o2)) + geom_density()

Code

mad_sheet%>% 
  mutate(o2 = cut2(order, cuts = c(9, 17, 25, 33, 41, 49, 57))) %>% 
  select(adj_rank_class, o2, Charlie:Tyler) %>% 
  jgt(., by = "o2", add.p = TRUE)

Characteristic	N	[ 1, 9), N = 81	[ 9,17), N = 81	[17,25), N = 81	[25,33), N = 81	[33,41), N = 81	[41,49), N = 81	[49,57), N = 81	[57,64], N = 81	p-value2
adj_rank_class	64									0.433
Nope		5 (62.5%)	4 (50.0%)	6 (75.0%)	6 (75.0%)	5 (62.5%)	6 (75.0%)	4 (50.0%)	8 (100.0%)
Top 20ish		3 (37.5%)	4 (50.0%)	2 (25.0%)	2 (25.0%)	3 (37.5%)	2 (25.0%)	4 (50.0%)	0 (0%)
Charlie	64									0.917
Mean (SD)		5.0 (2.3)	5.4 (1.4)	6.1 (1.7)	5.1 (1.8)	5.6 (1.5)	5.7 (1.6)	5.6 (1.6)	4.9 (1.1)
Median (IQR)		5.6 (3.6 - 6.7)	5.6 (4.4 - 6.0)	5.5 (4.6 - 7.7)	5.7 (4.4 - 6.2)	5.6 (4.4 - 6.8)	5.9 (4.7 - 6.8)	5.6 (4.4 - 7.0)	5.0 (4.2 - 5.7)
Range		1.2 - 7.7	3.2 - 7.5	4.2 - 8.5	1.2 - 6.9	3.5 - 7.7	3.2 - 8.1	3.3 - 7.5	3.2 - 6.5
Josh	64									0.497
Mean (SD)		5.9 (2.2)	6.8 (2.4)	5.6 (2.5)	4.9 (1.7)	5.7 (2.2)	5.7 (1.5)	6.2 (1.1)	5.5 (1.4)
Median (IQR)		6.4 (5.3 - 7.2)	6.9 (6.5 - 8.3)	6.5 (4.5 - 7.1)	5.4 (4.0 - 6.1)	5.7 (4.3 - 7.7)	5.9 (4.7 - 6.8)	6.3 (5.8 - 7.0)	5.6 (4.3 - 6.4)
Range		1.7 - 8.2	1.5 - 9.3	1.1 - 8.4	1.5 - 6.5	2.0 - 8.1	3.6 - 7.5	4.0 - 7.7	3.7 - 7.5
Justin	64									0.568
Mean (SD)		6.4 (1.6)	6.5 (1.4)	5.5 (1.5)	5.7 (1.4)	5.4 (0.9)	5.3 (1.9)	5.8 (1.1)	5.2 (1.8)
Median (IQR)		6.7 (4.9 - 7.6)	7.0 (6.3 - 7.4)	5.3 (5.0 - 5.9)	6.0 (4.8 - 6.8)	5.3 (5.0 - 5.8)	5.0 (4.0 - 7.0)	5.9 (5.4 - 6.3)	4.9 (3.9 - 6.6)
Range		4.5 - 8.5	4.0 - 7.6	3.0 - 8.0	3.5 - 7.0	4.0 - 6.9	2.5 - 8.0	4.0 - 7.5	3.0 - 8.0
Tyler	64									0.268
Mean (SD)		5.6 (1.7)	5.6 (1.9)	6.0 (1.6)	5.1 (1.5)	6.1 (2.5)	5.1 (1.9)	6.3 (1.2)	4.8 (0.7)
Median (IQR)		6.0 (4.8 - 6.4)	6.5 (4.8 - 7.0)	5.7 (5.0 - 6.5)	5.0 (5.0 - 5.8)	7.3 (4.8 - 8.0)	5.8 (3.8 - 6.3)	6.8 (5.8 - 7.1)	5.0 (4.0 - 5.1)
Range		2.5 - 7.5	1.9 - 7.0	4.0 - 9.0	2.0 - 6.9	1.3 - 8.0	2.0 - 7.0	4.0 - 7.5	4.0 - 5.5
1 n (%)
2 Fisher’s exact test; Kruskal-Wallis rank sum test

Code

mad_sheet%>% 
  mutate(o2 = cut2(order, cuts = c(9, 17, 25, 33, 41, 49, 57))) %>% 
  select(o2, Charlie:Tyler) %>% 
  pivot_longer(., -c(1)) %>% select(-name) %>% 
  jgt(., by = "o2", add.p = TRUE)

Characteristic	N	[ 1, 9), N = 32	[ 9,17), N = 32	[17,25), N = 32	[25,33), N = 32	[33,41), N = 32	[41,49), N = 32	[49,57), N = 32	[57,64], N = 32	p-value1
value	256									0.072
Mean (SD)		5.7 (2.0)	6.1 (1.8)	5.8 (1.8)	5.2 (1.6)	5.7 (1.8)	5.5 (1.7)	6.0 (1.2)	5.1 (1.3)
Median (IQR)		6.0 (4.5 - 7.2)	6.8 (4.9 - 7.1)	5.5 (5.0 - 7.1)	5.5 (4.4 - 6.5)	5.6 (4.5 - 7.3)	5.6 (4.0 - 6.9)	6.1 (5.4 - 7.0)	5.0 (4.0 - 5.9)
Range		1.2 - 8.5	1.5 - 9.3	1.1 - 9.0	1.2 - 7.0	1.3 - 8.1	2.0 - 8.1	3.3 - 7.7	3.0 - 8.0
1 Kruskal-Wallis rank sum test

2.5 By genre

Raw

Code

mad_sheet %>% select(Charlie:Tyler, g2) %>% na.omit() %>% jgt(., by = "g2", overall = TRUE, add.p = TRUE, spanner.size = 5, spanner.text = "Genre")

Characteristic	N	Genre					p-value1	Overall, N = 64
		alternative, indie & pop, N = 10	country, folk, americana & blues, N = 12	funk, soul and R&B, N = 8	punk, rock & metal, N = 19	rap & hip hop, N = 15
Charlie	64						<0.001
Mean (SD)		4.8 (0.5)	4.8 (1.8)	6.1 (1.2)	6.6 (1.3)	4.5 (1.6)		5.4 (1.6)
Median (IQR)		4.7 (4.4 - 5.2)	4.7 (3.8 - 6.0)	6.4 (5.7 - 6.8)	6.8 (5.7 - 7.5)	4.5 (3.4 - 5.8)		5.6 (4.4 - 6.7)
Range		4.2 - 5.5	1.2 - 7.5	4.2 - 7.7	4.1 - 8.5	1.2 - 7.1		1.2 - 8.5
Josh	64						0.041
Mean (SD)		5.7 (1.5)	5.2 (2.3)	6.6 (1.4)	6.5 (1.7)	4.9 (1.9)		5.8 (1.9)
Median (IQR)		5.6 (4.6 - 6.9)	5.4 (3.7 - 6.6)	6.6 (5.9 - 7.9)	6.6 (6.0 - 7.5)	5.5 (3.8 - 6.4)		6.0 (4.5 - 7.1)
Range		3.6 - 8.0	1.5 - 9.0	4.0 - 8.2	1.1 - 9.3	1.5 - 7.5		1.1 - 9.3
Justin	64						0.560
Mean (SD)		5.3 (1.7)	5.3 (1.3)	6.1 (1.2)	5.7 (1.6)	6.2 (1.5)		5.7 (1.5)
Median (IQR)		5.5 (4.6 - 6.6)	5.0 (4.4 - 6.3)	6.7 (5.4 - 6.9)	5.5 (4.7 - 7.0)	6.0 (5.0 - 7.2)		5.5 (4.8 - 7.0)
Range		2.5 - 7.6	3.5 - 7.3	4.0 - 7.5	3.0 - 8.0	4.0 - 8.5		2.5 - 8.5
Tyler	64						0.009
Mean (SD)		4.1 (1.8)	5.9 (1.6)	6.4 (1.4)	6.3 (1.3)	5.0 (1.6)		5.6 (1.7)
Median (IQR)		5.0 (2.5 - 5.0)	6.9 (4.8 - 7.0)	6.6 (5.4 - 7.5)	6.0 (5.5 - 7.0)	5.0 (4.0 - 5.9)		5.6 (5.0 - 7.0)
Range		1.3 - 6.0	3.0 - 7.5	4.0 - 8.0	4.0 - 9.0	2.0 - 8.0		1.3 - 9.0
1 Kruskal-Wallis rank sum test

Standardized

Code

mad_sheet %>% select(ends_with("_st"), g2) %>% na.omit() %>% jgt(., by = "g2", overall = TRUE, add.p = TRUE, spanner.size = 4, spanner.text = "Album picked by")

Characteristic	N	Album picked by				rap & hip hop, N = 15	p-value1	Overall, N = 64
		alternative, indie & pop, N = 10	country, folk, americana & blues, N = 12	funk, soul and R&B, N = 8	punk, rock & metal, N = 19
Charlie_st	64						<0.001
Mean (SD)		4.9 (0.7)	4.9 (2.5)	6.7 (1.6)	7.4 (1.8)	4.6 (2.2)		5.8 (2.2)
Median (IQR)		4.7 (4.4 - 5.5)	4.7 (3.6 - 6.5)	7.1 (6.2 - 7.6)	7.7 (6.2 - 8.6)	4.5 (3.0 - 6.2)		6.0 (4.3 - 7.5)
Range		4.1 - 5.9	0.0 - 8.6	4.1 - 8.9	4.0 - 10.0	0.0 - 8.1		0.0 - 10.0
Josh_st	64						0.041
Mean (SD)		5.7 (1.8)	5.0 (2.8)	6.8 (1.7)	6.7 (2.1)	4.7 (2.3)		5.8 (2.3)
Median (IQR)		5.5 (4.4 - 7.2)	5.2 (3.3 - 6.7)	6.8 (5.9 - 8.4)	6.8 (6.1 - 7.9)	5.4 (3.3 - 6.5)		6.1 (4.2 - 7.3)
Range		3.1 - 8.5	0.6 - 9.7	3.6 - 8.7	0.0 - 10.0	0.5 - 7.8		0.0 - 10.0
Justin_st	64						0.560
Mean (SD)		4.7 (2.8)	4.6 (2.1)	6.1 (2.0)	5.3 (2.6)	6.1 (2.4)		5.4 (2.4)
Median (IQR)		5.0 (3.5 - 6.8)	4.2 (3.1 - 6.3)	6.9 (4.8 - 7.4)	5.0 (3.7 - 7.5)	5.8 (4.2 - 7.8)		5.0 (3.8 - 7.5)
Range		0.0 - 8.5	1.7 - 8.0	2.5 - 8.3	0.8 - 9.2	2.5 - 10.0		0.0 - 10.0
Tyler_st	64						0.009
Mean (SD)		3.7 (2.3)	5.9 (2.0)	6.6 (1.9)	6.5 (1.7)	4.8 (2.0)		5.5 (2.2)
Median (IQR)		4.8 (1.6 - 4.8)	7.2 (4.5 - 7.4)	6.9 (5.3 - 8.1)	6.1 (5.5 - 7.4)	4.8 (3.5 - 6.0)		5.6 (4.8 - 7.4)
Range		0.0 - 6.1	2.2 - 8.1	3.5 - 8.7	3.5 - 10.0	0.9 - 8.7		0.0 - 10.0
1 Kruskal-Wallis rank sum test

2.6 Average rating

Raw

Code

mad_sheet %>% select(Charlie:Tyler, who) %>% 
  pivot_longer(., -c("who"), values_drop_na = TRUE) %>% select(value, who) %>% 
  jgt(., by = "who", add.p = TRUE, spanner.size = 4, spanner.text = "Album picked by")

Characteristic	N	Album picked by				p-value1
		Chuck, N = 64	Josh, N = 64	Justin, N = 64	Tyler, N = 64
value	256					<0.001
Mean (SD)		5.9 (1.8)	6.1 (1.5)	5.0 (1.7)	5.5 (1.5)
Median (IQR)		6.3 (4.5 - 7.0)	6.0 (5.0 - 7.2)	5.0 (4.0 - 6.0)	5.7 (4.8 - 6.7)
Range		1.1 - 9.0	2.3 - 9.3	1.2 - 8.5	1.2 - 8.0
1 Kruskal-Wallis rank sum test

Standardized

Code

mad_sheet %>% select(ends_with("_st"), who) %>% 
  pivot_longer(., -c("who"), values_drop_na = TRUE) %>% select(value, who) %>% 
  jgt(., by = "who", add.p = TRUE, spanner.size = 4, spanner.text = "Album picked by")

Characteristic	N	Album picked by				p-value1
		Chuck, N = 64	Josh, N = 64	Justin, N = 64	Tyler, N = 64
value	256					0.002
Mean (SD)		5.9 (2.6)	6.2 (2.0)	4.8 (2.3)	5.5 (2.1)
Median (IQR)		6.6 (4.1 - 7.5)	6.1 (4.8 - 7.9)	4.6 (3.5 - 6.1)	5.8 (4.5 - 7.3)
Range		0.0 - 10.0	1.5 - 10.0	0.0 - 10.0	0.0 - 8.9
1 Kruskal-Wallis rank sum test

2.7 Average rating by genre

Code

mad_sheet %>% select(Charlie:Tyler, g2) %>% 
  pivot_longer(., -c("g2"), values_drop_na = TRUE) %>% select(value, g2) %>% 
  jgt(., by = "g2", add.p = TRUE, spanner.size = 5, spanner.text = "Genre")

Characteristic	N	Genre					p-value1
		alternative, indie & pop, N = 40	country, folk, americana & blues, N = 48	funk, soul and R&B, N = 32	punk, rock & metal, N = 76	rap & hip hop, N = 60
value	256						<0.001
Mean (SD)		5.0 (1.5)	5.3 (1.8)	6.3 (1.3)	6.3 (1.5)	5.2 (1.7)
Median (IQR)		5.0 (4.4 - 5.8)	5.0 (4.0 - 7.0)	6.6 (5.5 - 7.3)	6.5 (5.5 - 7.4)	5.5 (4.0 - 6.2)
Range		1.3 - 8.0	1.2 - 9.0	4.0 - 8.2	1.1 - 9.3	1.2 - 8.5
1 Kruskal-Wallis rank sum test

2.8 Correlation among raters

Code

testRes <- corrplot::cor.mtest(mad_sheet %>% select(Charlie:Tyler) %>% na.omit() , conf.level = 0.95)
corrplot::corrplot(mad_sheet %>% select(Charlie:Tyler) %>% na.omit() %>% cor(), 
                   diag = FALSE, type = "lower", addCoef.col = "black", method = "color")

Code

mad_sheet %>% select(Charlie:Tyler) %>% na.omit() %>% 
  GGally::ggpairs(.,lower = list(continuous = "smooth", 
                                 wrap_fn_with_param_arg = c(method = "loess", fill = "blue"))) 

2.8.1 Correlation with All Music ratings

Code

testRes <- corrplot::cor.mtest(mad_sheet %>% select(Charlie:Tyler, all_music) %>% na.omit() , conf.level = 0.95)
corrplot::corrplot(mad_sheet %>% select(Charlie:Tyler, all_music) %>% na.omit() %>% cor(), 
                   diag = FALSE, type = "lower", addCoef.col = "black", method = "color")

2.8.2 Correlation with Rate Your Music ratings

Code

testRes <- corrplot::cor.mtest(mad_sheet %>% select(Charlie:Tyler, rym) %>% na.omit() , conf.level = 0.95)
corrplot::corrplot(mad_sheet %>% select(Charlie:Tyler, rym) %>% na.omit() %>% cor(), 
                   diag = FALSE, type = "lower", addCoef.col = "black", method = "color")

2.9 Person vs. group

Charlie

Code

mad_sheet %<>% mutate(t_minus_chuck = ((total - Charlie)/3)) %>% 
  mutate(t_minus_josh = (total - Josh)/3) %>% 
  mutate(t_minus_just = (total - Justin)/3) %>% 
  mutate(t_minus_tyler = (total - Tyler)/3)

ggplotly(mad_sheet %>% 
  ggplot(., aes(x = Charlie, y = t_minus_chuck, colour= who, text = album)) +geom_point() +
  geom_abline(intercept = c(0,0), slope = 1)+
  geom_abline(intercept = c(0,1), slope = 1, linetype = 2) +
  geom_abline(intercept = c(0,-1), slope = 1, linetype = 2 )+
  geom_abline(intercept = c(0,2), slope = 1, linetype = 3) +
  geom_abline(intercept = c(0,-2), slope = 1, linetype = 3))

Code

mad_sheet %>% select(Charlie, t_minus_chuck) %>% na.omit() %$% 
cor(Charlie, t_minus_chuck)

[1] 0.6654251

Code

mad_sheet %>% select(album,Charlie, t_minus_chuck) %>% 
  pivot_longer(., -1) %>% mutate(name = ifelse(grepl("minus", name), "Others", "Self")) %>% 
  select(name, value) %>% jgt(., by = "name", add.p = TRUE)

Characteristic	N	Others, N = 64	Self, N = 64	p-value1
value	128			0.342
Mean (SD)		5.7 (1.3)	5.4 (1.6)
Median (IQR)		5.8 (4.8 - 6.6)	5.6 (4.4 - 6.7)
Range		2.5 - 8.1	1.2 - 8.5
1 Wilcoxon rank sum test

Josh

Code

ggplotly(mad_sheet %>% 
  ggplot(., aes(x = Josh, y = t_minus_josh, colour= who, text = album)) +geom_point() +
  geom_abline(intercept = c(0,0), slope = 1, linetype = 1) +
  geom_abline(intercept = c(0,1), slope = 1, linetype = 2) +
  geom_abline(intercept = c(0,-1), slope = 1, linetype = 2 )+
  geom_abline(intercept = c(0,2), slope = 1, linetype = 3) +
  geom_abline(intercept = c(0,-2), slope = 1, linetype = 3)+ 
    coord_cartesian(xlim = c(1,9), ylim = c(1,9)))

Code

mad_sheet %>% select(Josh, t_minus_josh) %>% na.omit() %$% 
cor(Josh, t_minus_josh)

[1] 0.6446486

Code

mad_sheet %>% select(album,Josh, t_minus_josh) %>% 
  pivot_longer(., -1) %>% mutate(name = ifelse(grepl("minus", name), "Others", "Self")) %>% 
  select(name, value) %>% jgt(., by = "name", add.p = TRUE)

Characteristic	N	Others, N = 64	Self, N = 64	p-value1
value	128			0.177
Mean (SD)		5.6 (1.2)	5.8 (1.9)
Median (IQR)		5.6 (4.7 - 6.6)	6.0 (4.5 - 7.1)
Range		2.4 - 8.2	1.1 - 9.3
1 Wilcoxon rank sum test

Justin

Code

ggplotly(mad_sheet %>% 
  ggplot(., aes(x = Justin, y = t_minus_just, colour= who, text = album)) +geom_point() +
  geom_abline(intercept = c(0,0), slope = 1)+
  geom_abline(intercept = c(0,1), slope = 1, linetype = 2) +
  geom_abline(intercept = c(0,-1), slope = 1, linetype = 2 )+
  geom_abline(intercept = c(0,2), slope = 1, linetype = 3) +
  geom_abline(intercept = c(0,-2), slope = 1, linetype = 3) + 
    coord_cartesian(xlim = c(1,9), ylim = c(1,9)))

Code

mad_sheet %>% select(Justin, t_minus_just) %>% na.omit() %$% 
cor(Justin, t_minus_just)

[1] 0.5387147

Code

mad_sheet %>% select(album,Justin, t_minus_just) %>% 
  pivot_longer(., -1) %>% mutate(name = ifelse(grepl("minus", name), "Others", "Self")) %>% 
  select(name, value) %>% jgt(., by = "name", add.p = TRUE)

Characteristic	N	Others, N = 64	Self, N = 64	p-value1
value	128			0.722
Mean (SD)		5.6 (1.4)	5.7 (1.5)
Median (IQR)		5.5 (4.7 - 6.5)	5.5 (4.8 - 7.0)
Range		1.6 - 8.2	2.5 - 8.5
1 Wilcoxon rank sum test

Tyler

Code

ggplotly(mad_sheet  %>% 
  ggplot(., aes(x = Tyler, y = t_minus_tyler, colour= who, text = album)) +geom_point() +
  geom_abline(intercept = c(0,0), slope = 1)+
  geom_abline(intercept = c(0,1), slope = 1, linetype = 2) +
  geom_abline(intercept = c(0,-1), slope = 1, linetype = 2 )+
  geom_abline(intercept = c(0,2), slope = 1, linetype = 3) +
  geom_abline(intercept = c(0,-2), slope = 1, linetype = 3)+ 
    coord_cartesian(xlim = c(1,9), ylim = c(1,9)))

Code

mad_sheet %>% select(Tyler, t_minus_tyler) %>% na.omit() %$% 
cor(Tyler, t_minus_tyler)

[1] 0.4802801

Code

mad_sheet %>% select(album,Tyler, t_minus_tyler) %>% 
  pivot_longer(., -1) %>% mutate(name = ifelse(grepl("minus", name), "Others", "Self")) %>% 
  select(name, value) %>% jgt(., by = "name", add.p = TRUE)

Characteristic	N	Others, N = 64	Self, N = 64	p-value1
value	128			0.994
Mean (SD)		5.6 (1.4)	5.6 (1.7)
Median (IQR)		5.8 (4.8 - 6.7)	5.6 (5.0 - 7.0)
Range		2.2 - 8.2	1.3 - 9.0
1 Wilcoxon rank sum test

2.10 Distribution of ratings by album chooser

Code

mad_sheet %>% select(Charlie:Tyler, who) %>% 
  pivot_longer(., -c("who"), values_drop_na = TRUE) %>% select(value, who) %>% 
  ggplot(., aes(x = value, group = who, colour = who))+ geom_density() + 
  labs(title = "Album ratings", colour = "Album picked by") + theme_minimal()

2.11 Distribution of ratings by album rater

Code

mad_sheet %>% select(Charlie:Tyler, who) %>% 
  pivot_longer(., -c("who"), values_drop_na = TRUE) %>% select(value, name) %>% 
  ggplot(., aes(x = value, group = name, colour = name))+ geom_density() + 
  labs(title = "Ratings by person", colour = "Person rating")+ theme_minimal()

2.12 Album length impact on ratings

Code

mad_sheet %>% select(length, g2, Charlie:Tyler) %>% pivot_longer(., -c(1:2), values_drop_na = TRUE) %>% 
ggplot(., aes(x = length, y = value, colour = name, group = name)) + geom_point() +
  geom_smooth(method = "lm", se = FALSE)+ theme_minimal()

2.13 Album length impact on ratings by genre

Code

mad_sheet %>% select(length, g2, Charlie:Tyler) %>% pivot_longer(., -c(1:2), values_drop_na = TRUE) %>% 
ggplot(., aes(x = length, y = value, colour = g2, group = g2)) + geom_point() +geom_smooth(method = "lm", se = FALSE)+ theme_minimal()

2.14 Popularity impact on ratings

Code

mad_sheet %>% select(popularity, g2, Charlie:Tyler) %>% pivot_longer(., -c(1:2), values_drop_na = TRUE) %>% 
ggplot(., aes(x = popularity, y = value, colour = name, group = name)) + geom_point() +
  geom_smooth(method = "lm", se = FALSE)+ theme_minimal()

2.15 Popularity impact on ratings by genre

Code

mad_sheet %>% select(popularity, g2, Charlie:Tyler) %>% pivot_longer(., -c(1:2), values_drop_na = TRUE) %>% 
ggplot(., aes(x = popularity, y = value, colour = g2, group = g2)) + geom_point() +
  geom_smooth(method = "lm", se = FALSE)+ theme_minimal()

2.16 Album year impact on ratings

Code

mad_sheet %>% select(year, g2, Charlie:Tyler) %>% pivot_longer(., -c(1:2), values_drop_na = TRUE) %>% 
ggplot(., aes(x = year, y = value, colour = name, group = name)) + geom_point() +
  geom_smooth(method = "lm", se = FALSE)+ theme_minimal()

2.17 Album year impact on ratings by genre

Code

mad_sheet %>% select(year, g2, Charlie:Tyler) %>% pivot_longer(., -c(1:2), values_drop_na = TRUE) %>% 
ggplot(., aes(x = year, y = value, colour = g2, group = g2)) + geom_point() +geom_smooth(method = "lm", se = FALSE)+ theme_minimal()

2.18 Ratings by album

Code

mad_sheet %>% select(album, who, Charlie:Tyler) %>% na.omit() %>% pivot_longer(., -c(1:2)) %>% 
  group_by(album) %>% mutate(mean = mean(value), sd = sd(value)) %>% 
  ungroup() %>% 
  mutate(album = fct_reorder(album, mean, max)) %>% 
  ggplot(., aes(x = value, y = album, colour = name)) +geom_point() +theme_minimal()

2.19 Average ratings by album

Code

mad_sheet %>% select(album, who, Charlie:Tyler) %>% na.omit() %>% pivot_longer(., -c(1:2)) %>% 
  group_by(album) %>% mutate(mean = mean(value), sd = sd(value)) %>% 
  ungroup() %>% 
  mutate(album = fct_reorder(album, mean, max)) %>% 
  select(who, album, mean, sd) %>% distinct() %>% 
  ggplot(., aes(x = mean, y = album, colour = who)) +
  geom_point() +theme_minimal() + geom_errorbarh(aes(xmin = mean-sd, xmax = mean+sd, height = .01))

Code

mad_sheet %>% select(album, who, ends_with("_st")) %>% na.omit() %>% pivot_longer(., -c(1:2)) %>% 
  group_by(album) %>% mutate(mean = mean(value), sd = sd(value)) %>% 
  ungroup() %>% 
  mutate(album = fct_reorder(album, mean, max)) %>% 
  select(who, album, mean, sd) %>% distinct() %>% 
  ggplot(., aes(x = mean, y = album, colour = who)) +
  geom_point() +theme_minimal() + geom_errorbarh(aes(xmin = mean-sd, xmax = mean+sd, height = .01))

2.20 Average rating by standard deviation

Code

ggplotly(mad_sheet %>% select(artist, album, who, Charlie:Tyler) %>% na.omit() %>% pivot_longer(., -c(1:3)) %>% 
  group_by(album) %>% mutate(mean = mean(value), sd = sd(value)) %>% 
  select(artist, album, mean, sd, who) %>% distinct() %>% 
    mutate(text = paste(artist, album, sep = "\n")) %>% 
  ggplot(., aes(x = mean, y = sd, colour = who, text = text)) + geom_point() +theme_minimal())

3 Songs

Code

mad_songs %>% select(a1, album, track_name, y2, duration__ms_, popularity, genres:time_signature) %>% j.reactable()

Code

# fa <- data.frame(aa = list.files("jpeg")) %>% mutate(replace = gsub(".jpeg", ".png", aa))
# 
# for(i in 1:nrow(fa)){
#     img <- jpeg::readJPEG(paste0("jpeg/", fa$aa[i]))
#     Xx <- file.path("png/", fa$replace[i])
#     writePNG(img, Xx)
# }



# quarto publish quarto-pub madness.qmd

4 Regression

Code

dd <- datadist(mad_sheet); options(datadist='dd')

jm <- ols(Josh ~ who + g2 + length + year, data = mad_sheet %>% mutate(who = relevel(factor(who), "Josh")) )
cm <- ols(Charlie ~ who + g2 + length + year, data = mad_sheet %>% mutate(who = relevel(factor(who), "Chuck")) )
tm <- ols(Tyler ~ who + g2 + length + year, data = mad_sheet %>% mutate(who = relevel(factor(who), "Tyler")) )
jmm <- ols(Justin ~ who + g2 + length + year, data = mad_sheet %>% mutate(who = relevel(factor(who), "Justin")) )



trib.cols.rms <- tribble(~term, ~new, ~level, ~label,
             'who', 'Album picked by', '1', 'Album picked by',
             'g2', 'Genre', '2', 'Genre',
             'length', 'Length', '3', 'Length',
             'year', 'Year', '4', 'Year',
             'Charlie', 'Charlie', '5', 'Charlie',
             'Justin', 'Justin', '6', 'Justin',
             'Tyler', 'Tyler', '7', 'Tyler',
             'Josh', 'Josh', '8', 'Josh')

require(tibble)
prr <- rbind(
rms.sum.table3(summary(jm, year = c(1985, 2005), length = c(40, 60), who = "Josh"), 
               trib.cols.rms, anova(jm), raw = TRUE) %>% mutate(X = "Josh"),
rms.sum.table3(summary(cm, year = c(1985, 2005), length = c(40, 60), who = "Chuck"),
               trib.cols.rms, anova(cm), raw = TRUE) %>% mutate(X = "Chuck"),
rms.sum.table3(summary(tm, year = c(1985, 2005), length = c(40, 60), who = "Tyler"), 
               trib.cols.rms, anova(tm), raw = TRUE) %>% mutate(X = "Tyler"),
rms.sum.table3(summary(jmm, year = c(1985, 2005), length = c(40, 60), who = "Justin"), 
               trib.cols.rms, anova(jmm), raw = TRUE) %>% mutate(X = "Justin")) %>% 
  mutate(p = ifelse(term == "who", highc, axis)) %>% 
  mutate(f = if_else(term == "who", label, facet))

prr %>%
             select(X, label, levels, effect, conf.low, conf.high, any_of(c('d.f.', 'P'))) %>% 
             mutate(across(where(is.numeric), ~round(.x, 3))) %>% 
  group_by(X) %>% 
             jgtt(.)

label	levels	effect	conf.low	conf.high	d.f.	P
Josh
Album picked by	Chuck vs. Josh	-1.597	-2.867	-0.328	3	0.038
Album picked by	Justin vs. Josh	-1.485	-2.879	-0.092	3	0.038
Album picked by	Tyler vs. Josh	-1.602	-2.875	-0.328	3	0.038
Genre	alternative, indie & pop vs. punk, rock & metal	-0.800	-2.305	0.704	4	0.230
Genre	country, folk, americana & blues vs. punk, rock & metal	-1.423	-2.756	-0.090	4	0.230
Genre	funk, soul and R&B vs. punk, rock & metal	-0.132	-1.669	1.405	4	0.230
Genre	rap & hip hop vs. punk, rock & metal	-1.279	-2.779	0.221	4	0.230
Length	60 vs. 40	0.104	-0.767	0.975	1	0.812
Year	2005 vs. 1985	-0.176	-0.813	0.460	1	0.581
Chuck
Album picked by	Josh vs. Chuck	-0.630	-1.612	0.353	3	0.206
Album picked by	Justin vs. Chuck	-1.105	-2.193	-0.017	3	0.206
Album picked by	Tyler vs. Chuck	-0.877	-1.893	0.138	3	0.206
Genre	alternative, indie & pop vs. punk, rock & metal	-1.268	-2.432	-0.103	4	0.048
Genre	country, folk, americana & blues vs. punk, rock & metal	-1.492	-2.524	-0.461	4	0.048
Genre	funk, soul and R&B vs. punk, rock & metal	-0.558	-1.747	0.631	4	0.048
Genre	rap & hip hop vs. punk, rock & metal	-1.300	-2.461	-0.140	4	0.048
Length	60 vs. 40	-0.046	-0.720	0.628	1	0.892
Year	2005 vs. 1985	-0.362	-0.855	0.130	1	0.146
Tyler
Album picked by	Chuck vs. Tyler	-0.940	-2.000	0.120	3	0.012
Album picked by	Josh vs. Tyler	-0.757	-1.785	0.272	3	0.012
Album picked by	Justin vs. Tyler	-1.806	-2.861	-0.751	3	0.012
Genre	alternative, indie & pop vs. punk, rock & metal	-1.915	-3.130	-0.700	4	0.052
Genre	country, folk, americana & blues vs. punk, rock & metal	-0.541	-1.618	0.535	4	0.052
Genre	funk, soul and R&B vs. punk, rock & metal	-0.376	-1.617	0.865	4	0.052
Genre	rap & hip hop vs. punk, rock & metal	-0.864	-2.076	0.347	4	0.052
Length	60 vs. 40	0.085	-0.619	0.788	1	0.810
Year	2005 vs. 1985	-0.227	-0.741	0.287	1	0.380
Justin
Album picked by	Chuck vs. Justin	-0.438	-1.577	0.700	3	0.667
Album picked by	Josh vs. Justin	-0.118	-1.246	1.010	3	0.667
Album picked by	Tyler vs. Justin	-0.563	-1.621	0.495	3	0.667
Genre	alternative, indie & pop vs. punk, rock & metal	0.149	-1.069	1.367	4	0.180
Genre	country, folk, americana & blues vs. punk, rock & metal	-0.288	-1.367	0.791	4	0.180
Genre	funk, soul and R&B vs. punk, rock & metal	0.188	-1.056	1.432	4	0.180
Genre	rap & hip hop vs. punk, rock & metal	1.201	-0.013	2.416	4	0.180
Length	60 vs. 40	-0.686	-1.392	0.019	1	0.056
Year	2005 vs. 1985	-0.423	-0.939	0.092	1	0.105

Code

prr%>% mutate(across(where(is.numeric), as.numeric)) %>% 
    ggplot(., aes(x = effect, y = p, colour = X)) + geom_point(position = ggstance::position_dodgev(height = .55)) + 
    geom_errorbarh(aes(xmin = conf.low, xmax = conf.high), height = 0, position = ggstance::position_dodgev(height = .55)) +
    theme_classic() + 
    geom_vline(xintercept = 0, linetype = 2) + 
    labs(x = "Estimate", y = NULL, colour = element_blank()) +
    facet_grid(f~., scales = "free_y", switch = "y", space = "free")+
    theme(strip.text.y.left = element_text(angle = 0))+
    theme(strip.background  = element_blank()) +
    theme(panel.spacing.y = unit(0,"line")) +
    theme(strip.placement = 'outside') +
    theme(panel.grid.major.x = element_line(colour = "gray90"),
          panel.grid.minor.x = element_line(colour = "gray95",
                                            size = 0.3),
          plot.background = element_rect(fill = "white", size = 1.3)) 

Code

jx <- ols(Josh ~ Charlie +Justin +Tyler +who, data = mad_sheet %>% mutate(who = relevel(factor(who), "Josh")) )
cx <- ols(Charlie ~ Josh +Justin +Tyler +who, data = mad_sheet %>% mutate(who = relevel(factor(who), "Chuck")) )
tx <- ols(Tyler ~ Charlie +Josh +Justin +who, data = mad_sheet %>% mutate(who = relevel(factor(who), "Tyler")) )
mx <- ols(Justin ~ Josh +Charlie +Tyler +who, data = mad_sheet %>% mutate(who = relevel(factor(who), "Justin")) )

pxx <- rbind(
rms.sum.table3(summary(jx, who = "Josh", Charlie = c(5,6), Tyler = c(5, 6), Justin = c(5, 6)), 
               trib.cols.rms, anova(jx), raw = TRUE) %>% mutate(X = "Josh"),
rms.sum.table3(summary(cx, who = "Chuck", Josh = c(5,6), Tyler = c(5, 6), Justin = c(5, 6)),
               trib.cols.rms, anova(cx), raw = TRUE) %>% mutate(X = "Chuck"),
rms.sum.table3(summary(tx, who = "Tyler", Charlie = c(5,6), Josh = c(5,6), Justin = c(5, 6)), 
               trib.cols.rms, anova(tx), raw = TRUE) %>% mutate(X = "Tyler"),
rms.sum.table3(summary(mx, who = "Justin", Charlie = c(5,6), Josh = c(5,6), Tyler = c(5, 6)), 
               trib.cols.rms, anova(mx), raw = TRUE) %>% mutate(X = "Justin")) %>% 
  mutate(p = ifelse(term == "who", highc, axis)) %>% 
  mutate(f = if_else(term == "who", label, facet))

pxx %>%
             select(X, label, levels, effect, conf.low, conf.high, any_of(c('d.f.', 'P'))) %>% 
             mutate(across(where(is.numeric), ~round(.x, 3))) %>% 
  group_by(X) %>% 
             jgtt(.)

label	levels	effect	conf.low	conf.high	d.f.	P
Josh
Album picked by	Chuck vs. Josh	-1.634	-2.527	-0.742	3	<0.001
Album picked by	Justin vs. Josh	-1.695	-2.618	-0.771	3	<0.001
Album picked by	Tyler vs. Josh	-1.248	-2.143	-0.353	3	<0.001
Charlie	6 vs. 5	0.510	0.235	0.785	1	<0.001
Justin	6 vs. 5	0.603	0.360	0.845	1	<0.001
Tyler	6 vs. 5	-0.114	-0.373	0.144	1	0.379
Chuck
Album picked by	Josh vs. Chuck	-1.392	-2.167	-0.616	3	<0.001
Album picked by	Justin vs. Chuck	-1.080	-1.903	-0.256	3	<0.001
Album picked by	Tyler vs. Chuck	-1.498	-2.251	-0.745	3	<0.001
Justin	6 vs. 5	0.005	-0.246	0.256	1	0.968
Tyler	6 vs. 5	0.426	0.231	0.621	1	<0.001
Josh	6 vs. 5	0.382	0.176	0.588	1	<0.001
Tyler
Album picked by	Chuck vs. Tyler	-1.425	-2.352	-0.497	3	<0.001
Album picked by	Josh vs. Tyler	-0.829	-1.776	0.118	3	<0.001
Album picked by	Justin vs. Tyler	-1.935	-2.833	-1.038	3	<0.001
Charlie	6 vs. 5	0.590	0.319	0.860	1	<0.001
Justin	6 vs. 5	0.219	-0.071	0.509	1	0.137
Josh	6 vs. 5	-0.119	-0.387	0.150	1	0.379
Justin
Album picked by	Chuck vs. Justin	-1.068	-1.943	-0.193	3	0.011
Album picked by	Josh vs. Justin	-1.419	-2.278	-0.560	3	0.011
Album picked by	Tyler vs. Justin	-1.014	-1.901	-0.127	3	0.011
Charlie	6 vs. 5	0.006	-0.274	0.285	1	0.968
Tyler	6 vs. 5	0.176	-0.057	0.409	1	0.137
Josh	6 vs. 5	0.503	0.301	0.705	1	<0.001

Code

pxx%>% mutate(across(where(is.numeric), as.numeric)) %>% 
    ggplot(., aes(x = effect, y = p, colour = X)) + geom_point(position = ggstance::position_dodgev(height = .55)) + 
    geom_errorbarh(aes(xmin = conf.low, xmax = conf.high), height = 0, position = ggstance::position_dodgev(height = .55)) +
    theme_classic() + 
    geom_vline(xintercept = 0, linetype = 2) + 
    labs(x = "Estimate", y = NULL, colour = element_blank()) +
    facet_grid(f~., scales = "free_y", switch = "y", space = "free")+
    theme(strip.text.y.left = element_text(angle = 0))+
    theme(strip.background  = element_blank()) +
    theme(panel.spacing.y = unit(0,"line")) +
    theme(strip.placement = 'outside') +
    theme(panel.grid.major.x = element_line(colour = "gray90"),
          panel.grid.minor.x = element_line(colour = "gray95",
                                            size = 0.3),
          plot.background = element_rect(fill = "white", size = 1.3))

Code

lrm(I(raw_rank_class=="Top 20ish") ~ who + g2 + popularity +length +order +rym, data = mad_sheet  )

Logistic Regression Model

lrm(formula = I(raw_rank_class == "Top 20ish") ~ who + g2 + popularity + 
    length + order + rym, data = mad_sheet)

                      Model Likelihood      Discrimination    Rank Discrim.    
                            Ratio Test             Indexes          Indexes    
Obs            64    LR chi2     17.36      R2       0.334    C       0.810    
 FALSE         44    d.f.           11      R2(11,64)0.095    Dxy     0.619    
 TRUE          20    Pr(> chi2) 0.0976    R2(11,41.2)0.143    gamma   0.620    
max |deriv| 5e-07                           Brier    0.163    tau-a   0.270    

                                    Coef    S.E.   Wald Z Pr(>|Z|)
Intercept                           -6.3710 4.8152 -1.32  0.1858  
who=Josh                            -0.0904 0.8418 -0.11  0.9145  
who=Justin                          -1.6287 1.1065 -1.47  0.1410  
who=Tyler                           -0.4909 0.9811 -0.50  0.6168  
g2=country, folk, americana & blues  1.0759 1.4121  0.76  0.4461  
g2=funk, soul and R&B                1.3126 1.4299  0.92  0.3587  
g2=punk, rock & metal                2.4928 1.3822  1.80  0.0713  
g2=rap & hip hop                     1.8714 1.4227  1.32  0.1884  
popularity                           0.2090 0.4430  0.47  0.6371  
length                              -0.0257 0.0312 -0.82  0.4108  
order                               -0.0183 0.0185 -0.99  0.3238  
rym                                  1.3751 0.9477  1.45  0.1468  

Code

require(RankAggreg)
data("geneLists")

top25CE <- RankAggreg(geneLists, 25, seed=100, rho=.01)

mx <- ratings %>% select(1:5) %>% pivot_longer(., -1) %>% arrange(name, desc(value)) %>% 
  group_by(name) %>% select(-value) %>% mutate(rank = 1:n()) %>% pivot_wider(., names_from = "rank", values_from = "album") %>% 
  as.matrix()

rownames(mx) <- mx[,1]
mx <- mx[, -1]


top20 <- RankAggreg(mx, nrow(ratings), seed=100, rho=.01, verbose = FALSE)
rank_agg <- data.frame(album = top20$top.list) %>% mutate(rank_agg = 1:n())

# topxx <- RankAggreg(mx[,1:20], 20, seed=100, rho=.01, verbose = FALSE)